MiRNAs are small (~22nt long) non-coding RNA sequences; binds to the complementarity target sites in 3' Untranslated Region (UTR) of mRNA sequences but not restricted to other mRNA regions viz., 5' UTR and Coding sequences (CDS). Complementarity binding of miRNA to mRNA target sites either results in complete degradation of the mRNA itself or it may regulate the mRNA as an oncogene or as a tumo...

Cost-Sensitive Learning is a type of learning in data mining that takes the misclassification costs (and possibly other types of cost) into consideration. The goal of this type of learning is to minimize the total cost. The key difference between cost-sensitive learning and cost-insensitive learning is that cost-sensitive learning treats the different misclassifications differently. Costinsensi...

One of the more challenging problems faced by the data mining community is that of imbalanced datasets. In imbalanced datasets one class (sometimes severely) outnumbers the other class, causing correct, and useful predictions to be difficult to achieve. In order to combat this, many techniques have been proposed, especially centered around sampling methods. In this paper we propose an ensemble ...

In medical diagnosis, the problem of class imbalance is popular. Though there are abundant unlabeled data, it is very difficult and expensive to get labeled ones. In this paper, an ensemble-based active learning algorithm is proposed to address the class imbalance problem. The artificial data are created according to the distribution of the training dataset to make the ensemble diverse, and the...

It has been observed that class imbalance (that is, significant differences in class prior probabilities) may produce an important deterioration of the performance achieved by existing learning and classification systems. This situation is often found in real-world data describing an infrequent but important case. In the present work, we perform a review of the most important research lines on ...

The class imbalance problem in two-class data sets is one of the most important problems. When examples of one class in a training data set vastly outnumber examples of the other class, standard machine learning algorithms tend to be overwhelmed by the majority class and ignore the minority class. There are several algorithms to alleviate the problem of class imbalance in literature. In this pa...

MicroRNAs (miRNAs) are ~20-25 nucleotides non-coding RNAs, which regulated gene expression in the post-transcriptional level. The accurate rate of identifying the start sit of mature miRNA from a given pre-miRNA remains lower. It is noting that the mature miRNA prediction is a class-imbalanced problem which also leads to the unsatisfactory performance of these methods. We improved the predictio...

A well balanced dataset is very important for creating a good prediction model. Medical datasets are often not balanced in their class labels. Most existing classification methods tend to perform poorly on minority class examples when the dataset is extremely imbalanced. This is because they aim to optimize the overall accuracy without considering the relative distribution of each class. In thi...